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Environ. Sci. Technol. 2005, 39, 9115-9122
Passive and Active Air Samplers as Complementary Methods for Investigating Persistent Organic Pollutants in the Great Lakes Basin T . G O U I N , * ,† T . H A R N E R , ‡ P. BLANCHARD,‡ AND D. MACKAY† Canadian Environmental Modelling Centre, Trent University, Peterborough, Ontario, K9J 7B8, Canada, and Meteorological Service of Canada, Environment Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada
Data obtained using passive air samplers (PAS) are compared to active high-volume air sampling data in order to assess the feasibility of the PAS as a method, complementary to active high-volume air sampling (AAS), for monitoring levels of organochlorine (OC) pesticides, polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) in the Laurentian Great Lakes. PAS were deployed at 15 of the Integrated Atmospheric Deposition Network (IADN) sites on a quarterly basis between July 2002 and June 2003, and PAS and AAS results are compared. Levels for the OC pesticides are typically highest in agricultural areas, with endosulfan I dominating air concentrations with values ranging between 40 and 1090 pg‚m-3, dieldrin values between 15 and 165 pg‚m-3, and γ-HCH values between 13 and 100 pg‚m-3. R-HCH was seen to be relatively uniform across the Great Lakes Basin with values ranging between 15 and 73 pg‚m-3. Large urban centers, such as Chicago and Toronto, have the highest levels of PCBs and PBDEs that range between 400 and 1200 pg‚m-3 and 10 and 70 pg‚m-3, respectively. Comparison of the AAS and the PAS data collected during this study shows good agreement, within a factor of 2 or 3, suggesting that the two sample methods produce comparable results. It is suggested that PAS networks, while providing data that are different in nature from AAS, can provide a cost-effective and complementary approach for monitoring the spatial and temporal trends of persistent organic pollutants.
1. Introduction A major goal of mapping the spatial and temporal distribution of POPs in air is to assess how regulatory initiatives are influencing the environmental levels of banned substances, and to identify potential source regions or “hot spots” (1). Historically there has been a reliance on the periodic collection of high-volume air samples to determine these changes. For instance, using five years of monitoring data collected in the Canadian Arctic, Hung et al. (2) demonstrated that levels for several of the lower chlorinated biphenyls are declining. The implication is that regulatory activities that * Corresponding author phone: 705-748-1005; fax: 705-748-1080; e-mail: [email protected] † Trent University. ‡ Environment Canada. 10.1021/es051397f CCC: $30.25 Published on Web 11/02/2005
2005 American Chemical Society
have been initiated with respect to the use and manufacture of the polychlorinated biphenyls (PCBs) in temperate industrial regions have resulted in reduced levels in the Arctic. A notable illustration of the importance for the long-term collection of air samples is the success of the Integrated Atmospheric Deposition Network (IADN), which is an international joint venture between Environment Canada and the U. S. EPA’s Great Lakes National Program Office (3). This monitoring network is composed of both master and satellite stations that are located throughout the Laurentian Great Lakes, and has collected 24-hour air samples every 12th day since 1990 at each of its master stations. The data collected by IADN have established long-term trends for a variety of organic pollutants, including the polycyclic aromatic hydrocarbons (PAHs), PCBs, and several organochlorine (OC) pesticides. It has also identified potential source regions for these contaminants to the Great Lakes (4). Recently, passive air samplers (PAS) have been deployed at various geographic scales, from local to continental, to assess the distribution of POPs in air (5-10). Various materials have been used as passive sampling media, including polyurethane foam (PUF) disks, semipermeable membrane devices (SPMDs), polymer-coated glasses (POGs), and XAD-2 resin, a styrene-divinylbenzene copolymer typically used in the collection of high-volume air samples. Recent developments have improved their use as quantitative tools. Notable is the use of depuration compounds to determine sample rates (11-13). Consequently, we suggest that a method complementary to the collection of high-volume air samples for monitoring long-term trends is PAS, which can enable the cost-effective collection of monthly or seasonally integrated air samples deployed throughout a large geographic region. In this study, the feasibility of using PAS to determine the seasonal and spatial distribution of various POPs in the Laurentian Great Lakes is investigated. During this one-year pilot study (July 2002 to June 2003) PAS were deployed on a seasonal basis at a number of the IADN stations, including both urban and rural sites, with a view to identify both potential source regions and seasonal trends. Data obtained from the PAS are then compared with active high-volume air samples (AAS) that were collected at several of the sites during the study period, thus demonstrating the feasibility of the PAS as an effective atmospheric monitoring tool.
2. Experimental Section Sampler Design and Theory. PUF disks, consisting of the same polyurethane foam typically used in the sorption of gas-phase organics in high-volume air samplers, were housed in a stainless steel domed chamber (5). The uptake of organic contaminants by the PUF disks from the atmosphere has been described in previous studies (5, 14), and can be modeled by assuming that the PUF disk is a uniform, porous compartment into which gaseous chemicals can penetrate. Briefly, the uptake of contaminants by passive sampling media has been shown to be largely controlled by the airside mass transfer coefficient, kA (m‚h-1), which is a weak function of temperature, but is strongly influenced by wind speed (14). Laboratory and wind tunnel experiments, however, have shown that the effect of wind speed on the sampling rate is diminished due to the design of the sampling chamber (5, 9, 15). A major challenge in understanding data obtained from PAS is estimating an equivalent air sample volume. This provides a means of estimating an air concentration, which can allow the comparison between PAS deployed at various VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
FIGURE 1. Position of sampling sites in relation to population density. locations and times. To assess the kinetics of the sampling rates of individual PAS quantitatively, depuration compounds (DCs) were added to each of the PUF disks prior to their deployment and the extent of their loss was measured in terms of their individual recoveries. Ideally the DCs should have recoveries between 20 and 80% of their initial amount to enable the linear sampling rate of individual PAS to be determined during the deployment period (16). This requires DCs with a wide range of octanol-air partition coefficients (KOA). The following DCs were used in this study; 2,4,6trichlorobiphenyl (PCB-30), deuterated γ-hexachlorocylcohexane (d6γ-HCH), 2,3,3′,4,5-pentachlorobiphenyl (PCB-107), and 2,2′,3,3′,4,5,5′,6-octachlorobiphenyl (PCB-198). The flux, N (pg‚h-1), of the DC from the PUF disk, resulting in a decrease in the initial concentration, C0 (pg‚m-3), is given by
N ) C0·A·kA/KPUF-A ) -VdC/dt
where A is the planar area of the exposed portion of the PUF disk (0.037 m2), V is the volume of the PUF disk (0.00021 m3), and KPUF-A is the PUF disk-air partition coefficient of the DC, which is similar in magnitude to KOA. This assumes that the atmospheric concentration is zero and the PUF disk-air partition coefficient is related to KOA (14, 17). If the effective thickness of the PUF disk is given by δ (m), which is equal to V/A, then
dC/dt ) -C0·kA/(δKPUF-A) ) -C0·kd
where kd (h-1) is the depuration rate constant. Integrating from an initial concentration (C0), and rearranging eqs 1 and 2, gives at time, t
where Ct (pg‚m-3) is the concentration at the end of the deployment of the sampler. Assuming that the influence of wind speed on the clearance of the DCs is minimized by the sampling chamber, kA will remain constant during the deployment period. The temperature dependence of the partition coefficient, KPUF-A, for the DCs may vary significantly, and must be considered when estimating kA. This can be estimated by first temperature-correcting the KOA of the DC and using the relationship described by Shoeib and Harner (14, 17) to calculate KPUF-A. Because the temperature dependence of the partition coefficient is not linear, using the average temperature for the deployment period to correct KPUF-A may not adequately capture the temperature dependence of the partitioning of the DC. This is particularly important for samples deployed in the Great Lakes region, where temperatures can vary as much as 40 °C during a deployment period. Thus, KPUF-A should be temperature-corrected for every day the sample was deployed, based on the daily temperature recorded at each of the sites. The average KPUF-A estimated for the deployment period should be used to estimate kA. Sampling Sites. PAS were deployed throughout the Laurentian Great Lakes on a seasonal basis between July and October 2002 (period 1, i.e., summer), October and December 2002 (period 2, i.e., autumn), January and March 2003 (period 3, i.e., winter), and March and June 2003 (period 4, i.e., spring). Several IADN stations were used as sampling sites, including five master stations, located at Burnt Island (BNT), Eagle Harbor (EGH), Sleeping Bear Dunes (SBD), Point Petre (PPT), and Sturgeon Point (STP) and seven satellite stations, located at Chicago (CHI), St. Clair (STC), Point Pelee (PPL), Burlington (BUR), Rock Point (RPT), Egbert (EGB), and Grand Bend (GDB) (3). In addition, 3 other sites, located in Toronto (TOR), Downsview (DOW), and at a field research site operated by Trent University (TNT) (18), were included. Figure 1 illustrates the locations of these 15 sites in relation to population density.
Sampling Procedure. PUF disks were first cleaned with water and then Soxhlet extracted, first for 24 h with acetone, then by two 24-h extractions using petroleum ether. The PUF disks were dried in a desiccator under vacuum for 24 h before being spiked with the suite of DCs. This was achieved by first spiking 20 mL of petroleum ether with 100 µL of the DC mixture and applying this evenly to both sides of the disk using a Pasteur pipet. The solvent was evaporated (approximately 10 min) before storing the disk in solvent-rinsed jars with Teflon lined lids. The PUF disks were then shipped to each of the individual IADN stations where they were deployed. Duplicate samples were collected at TOR, DOW, EGB, and PPT. In addition, 21 field blanks, which were prepared and handled in a manner identical to the samples, were also collected from each of the sites on a rotating basis. Extraction and Quantification. The PUF disks were Soxhlet extracted for 18 h with 250 mL of petroleum ether. The DCs, PCB-107, and PCB-198, were used as surrogates for assessing method recoveries. Because of their low volatilities, these compounds should experience negligible loss from the PUF disks during the deployment period (10). Extracts were reduced by rotary evaporation and nitrogen blow down to 500 µL and placed on a 1-g alumina column deactivated with 6% distilled water, pre-washed with 20 mL of 5% dichloromethane (DCM) in petroleum ether, with 3 × 0.5 mL washings of petroleum ether. The compounds of interest were eluted through the column with 20 mL of the 5% DCM/ petroleum ether solution and then reduced under nitrogen to 500 µL. This was then solvent exchanged into isooctane. Mirex was added as an internal standard and the sample was reduced under nitrogen to 500 µL for injection on the gas chromatograph-mass spectrometer (GC-MS). Air sample extracts were quantified for 19 OC pesticides, 48 PCB congeners, and 13 PBDE congeners using external standard solutions (10). Details regarding instrument method and analysis are reported elsewhere (10).
3. Results and Discussion QA/QC. All data have been blank corrected based on the collection and analysis of 21 field and 13 method blanks (i.e., solvent blanks). The field blanks for the PUF disks were characterized by higher levels for the PBDEs and PCBs than was found in the method blanks, which were characterized by undetectable levels of both the PCBs and PBDEs. Field blanks for the heavier PCB congeners (>tetraCB) and OC pesticides were typically below the instrument quantification limit (2.5 pg‚µL and 0.32 pg‚µL, respectively), whereas the lighter PCB congeners (75% (Table S1 in the Supporting Information), thus no correction was applied. These recoveries are comparable with those of previous studies conducted in the same lab (14). The efficiency of the extraction and cleanup method, tested by adding the PCB and PBDE standard solutions to the PUF disks prior to extraction, have also been reported elsewhere with recoveries >75% (5, 10). Quality assurance measures included the collection of 24 duplicate samples in total, with 6 of the samples being extracted and analyzed separately by the Organic Analysis Laboratory at Environment Canada in Toronto following the IADN method protocol (19). Duplicates were collected and analyzed to provide an indication of the overall precision of both the sampling and laboratory methods. Duplicates with
a coefficient of variance (COV) that is <35% indicate very good agreement between paired samples (i.e., a COV <35% indicates concentration differences that are about a factor of 1.5 between paired samples). Results for the deployment of all duplicate samples for PCBs, PBDEs, and four OC pesticides are reported in Table S2 (Supplementary Information), where 81% of duplicates had a COV <35%, and 91% had a COV <50% for the PCBs and OC pesticides. While the agreement for the analysis of the PCBs and OC pesticides is generally good, and implies that a factor of two difference in concentration between PAS is significant, the variability in the duplicates observed for the PBDEs is more difficult to interpret, primarily due to the low number of samples. PAS Air Volumes. By measuring the loss of the DCs at the end of the sampling period, and assuming that the depuration rate will be the same as the rate of uptake for a given chemical (12), the relationship described in eq 4 can be used to estimate kA, from which a sampling rate, R, can be estimated as kA‚A (14). Figure S1 (Supplementary Information) summarizes R for the PAS at the sampling sites for each deployment period. The mean sampling rates ranged from between 1.5 and 5.7 m3‚d-1, with an average sampling rate of 3.1 m3‚d-1 (Figure S1). Typically, the variability in the sampling rate between seasons for each individual site was within 25% of the mean value (Figure S1). Sampling rates for the summer period are based on the recovery of d6γ-HCH, which has recoveries within the desired range of between 20 and 80%, whereas the recoveries of PCB30 during this period are less than 20%, primarily due to its high volatility at the warmer temperatures. During the spring and autumn periods the sampling rate is based on either PCB-30 or d6γ-HCH, or is an average of the two, depending on the measured recovery of the DCs. During the winter deployment period there was negligible loss of the DCs, primarily due to their low volatility at the cooler temperatures, and it was necessary to estimate the sampling rates as the average from the other sampling periods. We thus recommend caution when interpreting data reported for this period. To improve the effectiveness of the DCs in future studies to more accurately assess the sampling rate, we recommend that a wider suite of compounds be used. In particular we suggest the use of compounds with lower KOA values to better address the influence of temperature on the partitioning characteristics during the winter or at sites characterized by low air temperatures, such as in the Arctic. During the summer or at sampling sites characterized by warm temperatures, compounds with higher KOA values would prove more useful. A summary of the loss of DCs added to the PUF disks, the average KPUF-A, and the deployment time for each sample used in calculating kA are provided in Table S1 (Supplementary Information). Passive Air Concentrations. To simplify the presentation and interpretation of results, annual average air concentrations for a select number of OC pesticides, including R-HCH, γ-HCH, endosulfan I, and dieldrin, a condensed list of PCB (n ) 11) and PBDE congeners (n ) 6), representing approximately 50% of individual sample concentrations of 48 PCB congeners and approximately 95% of 13 PBDE congeners, are reported in Table 1. The ΣPCB11 annual average values for the one-year pilot study ranged between 15 and 960 pg‚m-3 (Table 1), with higher values associated with the urban sampling sites and the lower values occurring at the more remote locations. Similar results were obtained for the PBDEs, where the ΣPBDE6 ranged between <3 and 37 pg‚m-3. Atmospheric levels of R-HCH were observed to be generally uniform across the Great Lakes basin, ranging between 15 and 73 pg‚m-3, whereas γ-HCH was observed to be higher in rural, agricultural areas, with values ranging between 13 and 100 VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
TABLE 1. Annual Average Concentrations (pg‚m-3) of OC Pesticides and PCB and PBDE Congeners in PAS Collected at Sites near the Great Lakes between July 2002 and June 2003
a Average air concentration reported for 12 AAS samples collected between 1997 and 1999 (30). b Average AAS air concentration reported between 1991 and 1995 (20). c PAS air concentrations calculated using PUF disk as a passive air sampler between July and October 2000 (5). d Average PAS air concentration reported for the month of April 2002, with the calculated concentration in air values using PUF disk as a passive air sampler during the month of April 2002 in parentheses (15).
pg‚m-3. Elevated levels of endosulfan I were observed throughout southern Ontario, with annual average values ranging between 33 and 430 pg‚m-3. Levels of dieldrin were observed to be highest at CHI, with values ranging between 15 and 165 pg‚m-3. OC Pesticides. The spatial and temporal distributions of R- and γ-HCH are shown in Figure 2. The uniform distribution of R-HCH is consistent with previous observations (9) and is expected based on its relatively high volatility and persistence, enabling it to attain fairly uniform concentrations globally (20). The results for R-HCH lend confidence to the PAS dataset, since concentrations of R-HCH are consistent with previous observations reported for the Great Lakes Basin. Active, high volume, air samples (AAS) were collected at all IADN master stations, Egbert, and Chicago once every 12 days during the passive sampling campaign. Although the IADN data are not continuously integrating the air burden of POPs, as do the PAS, they do provide a basis for comparing results. Figure 3 compares AAS results (21) with PAS data for R-HCH at IADN stations located at EGH, CHI, SBD, and STP for the four deployment periods. The AAS data were averaged to correspond to the deployment period of the PAS. The error bars for the high-volume air data represent the standard deviation of the averaged concentrations. The agreement is quite good (i.e., within a factor of 2-3) especially considering the temporal variability (error bars) of the AAS data. γ-HCH, which is the major constituent of lindane, was in use but was being phased out in Canada in 2003. Thus, the elevated levels of γ-HCH during the spring (Figure 2b) are most likely associated with agricultural activities in the region. 9118
Concentrations of both R- and γ-HCH are consistent with ambient concentrations in the Great Lakes region (1, 5, 9). Endosulfan I (Figure 2c) is the most dominant OC pesticide in air during the summer and spring deployment periods, and is found to be mostly associated with samples collected in southwestern Ontario, which is a large agricultural region. Endosulfan has been used as an insecticide agriculturally since the mid 1950s, and is effective against a wide range of insects. Thus, the elevated levels of Endosulfan I in these regions during the most agriculturally active periods is expected. The concentrations reported in Figure 2c for Endosulfan I are consistent with previous observations. For instance, Harner et al. (5) reported levels of Endosulfan I ranging between 250 and 820 pg‚m-3 in southern Ontario. This agrees favorably with levels observed in this study for the summer and spring deployment periods, when concentrations ranged between 40 and 1090 pg‚m-3. The use of dieldrin has been restricted since the late 1980s. It is thus believed that atmospheric levels of dieldrin are most likely associated with volatilization from sources associated with historical usage (5). Results for dieldrin shown in Figure 2d indicate elevated levels are mostly associated with both urban and rural agricultural areas where usage would have been most widespread. A strong seasonal trend, with elevated concentrations in the warmer spring and summer deployment periods is also apparent. PCBs. The spatial and temporal distributions of PASderived PCB air concentrations are shown in Figure 4a. Results agree well with previous studies in this region. For instance, Harner et al. (5) reported PCB concentrations along an urban-
FIGURE 2. Spatial and temporal distribution of organochlorine pesticides (a) r-HCH and (b) γ-HCH and (c) endosulfan I and (d) dieldrin in air in the Laurentian Great Lakes between July 2002 and June 2003.
FIGURE 3. Comparison of r-HCH air concentrations between PAS and AAS for each of the deployment periods: (a) summer; (b) autumn; (c) winter; (d) spring. Error bars for the AAS indicate the observed standard deviation. rural gradient, using PAS, extending from Toronto northward through south-central Ontario. ΣPCB was 1350 pg‚m-3 in Toronto and about an order of magnitude lower, ranging
from between 135 and 270 pg‚m-3, at rural sites (5). Other high-volume air sampling studies have reported PCB air concentrations in Chicago >1000 pg‚m-3, particularly during VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
FIGURE 4. Spatial and temporal distribution of (a) ΣPCB11 and (b) ΣPBDE6 in air in the Laurentian Great Lakes between July 2002 and June 2003. the summer, with levels at more remote locations in the Great Lakes region being about an order of magnitude lower (3, 22-24). The PCB congener profile in the PAS collected in the urban areas is also consistent with previous studies showing a higher abundance of the heavier congeners at the urban sites (indicating potential primary sources at these sites) and a lower molecular weight composition, enriched in more volatile PCB congeners at the more remote locations (Table 1). This is consistent with the “urban fractionation” effect observed for the Toronto, urban-rural gradient study (5). In general, the spatial trends for the PAS-derived PCB air concentrations presented here are consistent with other measurements in the Great Lakes Basin (Table 1) (18, 23, 25-28). Temporal trends for PCBs vary among sites (Figure 4a). Sites that are in close proximity to densely populated areas, and hence potential inputs from primary sources (TOR, STP, 9120
CHI, BUR, DOW, PPL, RPT, PPT, EGB, BNT), exhibit maximum air concentrations during the warmer periods and lowest concentrations during the winter. This is typical of source regions (26, 29, 30). A few sites showed maximum concentrations during the autumn (STC, TNT) and springtime (GDB) periods. At the more remote sites (EGH, SBD) the seasonality is suppressed, as air concentrations will rely more heavily on advection from outside regional sources. PBDEs. Congener specific average air concentrations for the PBDEs are reported in Table 1. The PAS-derived values are consistent with previously reported values at several of the U. S. IADN stations using high-volume air samplers (31). Analogous to the PCB data presented above, the PBDE data also indicate a strong urban-rural gradient (Figure 4b), which is also consistent with the results reported for several studies conducted in the Laurentian Great Lakes, implying that urban sites are potential sources of PBDEs (18, 31, 32). This
FIGURE 5. 3-Day back trajectories for each day AAS were collected at STP during the PAS (a) summer and (b) spring deployment periods, and comparison between AAS and PAS for OC pesticides and PCBs. observation is also in good agreement with a number of European studies which have also examined the spatial distribution of PBDEs (6, 33). PAS and AAS comparison. When comparing between PAS and AAS data for the PCBs, a large discrepancy can be seen for the summer deployment period (Figure 5). In this instance, it is informative to examine air parcel back trajectories for the days when AAS were collected ir to determine if the AAS are representative of the entire period integrated by the PAS. Figure 5 shows the 3-day back-trajectories for STP for each day AAS were collected, the temporal trend for AAS PCB data (inset) and a comparison of AAS and PAS data for PCBs and selected OC pesticides. Previous studies have shown that sites to the south and east of STP are source regions of PCBs (4). However, of the five AAS collected during the summer period, only one sample, collected on August 31, is shown to be sampling air that has passed over this region and exhibited higher PCB levels. The back trajectory probability density maps for the PAS collected at STP (Supplementary Information), on the other hand, indicate that the air intregrated by the PAS for the entire period shows a much larger contribution from the PCB source regions (4). This explains the higher levels of PCBs in the PAS versus the AAS in Figure 5a. In contrast, an example of where the AAS and PAS data agree well is shown in Figure 5b for STP during the spring period. In this case the air parcel back trajectories are consistent with the back trajectory probability maps for the entire period (Supplementary Information). Generally, the
good agreement between the AAS and PAS data shown in this study suggests that the PAS can be reliably used to monitor the spatial and temporal trends of POPs, complementing the data obtained from the AAS. In summary, PAS can serve as a reconnaissance tool or an inexpensive method for monitoring POPs as a complementary method to AAS. Probability density maps for air parcel trajectories can complement the interpretation of PAS data and lead to insights regarding source-receptor relationships for POPs.
Acknowledgments We thank the Natural Sciences and Engineering Research Council of Canada (NSERC), Environment Canada, and the consortium of companies that support the Canadian Environmental Modelling Centre for financial support, Ron Hites, Ilora Basu, and Celine Audette for providing IADN highvolume air data, Helena Dryfhout-Clark (EC) for meteorological data, Jacinthe Racine (EC) for backward and forward trajectory data, Ken Brice and Ky Su (EC) for participating in inter-laboratory study, and Celine Audette and the IADN site operators for deployment and handling of PAS. We are also grateful for the thoughtful review by Jochen Mu ¨ ller.
Supporting Information Available Two tables summarizing results for duplicate samples and depuration compounds, one figure summarizing the sampling rates for individual samplers, and detailed air trajectory VOL. 39, NO. 23, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
probability density maps for each season samplers were deployed at each of the sites. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review July 18, 2005. Revised manuscript received August 29, 2005. Accepted August 30, 2005. ES051397F